Tailored support may reduce mental and relational impact of infertility on infertile patients and partners
Bibliographic record
Abstract
RESEARCH QUESTION: What is the psychological impact of infertility on infertile patients and partners of infertile patients? DESIGN: This online, international, quantitative survey assessed the impact of infertility on mental health, relationships and daily activities for 1944 respondents. Respondents were male or female infertile patients (n = 1037) or partners to infertile patients (n = 907; not necessarily partners of the patient sample) and were recruited at different stages of the treatment journey. RESULTS: The most common emotions were 'sadness' at infertility diagnosis and 'anxiety' during treatment. Emotions differed in nature and intensity throughout the journey. Envy of others who achieved pregnancy was frequently reported by women. More than half of respondents (60.4%; n = 1174) perceived the infertility journey to have impacted their mental health, and 44.1% (n = 857) of respondents sought mental health support. More patients reported mental health impacts (70.1%, n = 727) than partners (49.3%, n = 447). One in three respondents indicated that their relationship had suffered due to the infertility diagnosis. Of these respondents, 55.0% (n = 409) strongly agreed that infertility caused an emotional strain. Patients more often than partners reported a detrimental impact on daily activities. Respondents most commonly agreed with statements regarding an 'effect on work-life balance'. CONCLUSION: Treatment journey stages are defined by their impact profile, which differs between infertile patients and partners of infertile patients. Negative impacts are diverse (mental health, relational, daily activities). There was disparity between the number of respondents reporting mental health issues and the number seeking mental health support. This indicates the need for support services tailored to different treatment stages.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".